As the Internet becomes more pervasive, the content and services are increasing in quantity as well as improving in quality. This trend is fostered by the advancement of technologies, such as RESTful services, Web 2.0, and Mashups. Service composition integrates services to fulfill specific tasks using a set of tools. The existing service composition techniques and tools are mainly designed for the Service Oriented Architecture (SOA) professionals. The business processes used in the service composition systems are primarily designed by business analysts who have extensive process knowledge. Due to the lack of process knowledge, novice business analysts and end users face challenge to identify and orchestrate service into a well-defined business process. Even for the experienced users, it is challenging to select appropriate services from a set of functionally similar services as the quality information of services may not be available. In this thesis, we propose a framework that allows a non-technical user to combine web services to achieve a goal. Our approach helps users to find the process knowledge from the web. We index web services based on the semantic concepts available in the service description documents and help users to formulate a web service search query. We use online reviews to choose a web service from a set of functionally similar web services. Our approach automatically finds the data flow between web services and generates a user interface to execute a composite service. The effectiveness of our proposed approaches is demonstrated through a series of case studies. The results of our case studies show that our approaches for process knowledge extraction, service discovery, and service selection make it easier for people with less technical knowledge to compose services. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2014-06-30 15:18:28.155

The constant growth in network communications technologies and the emergence of Service Specific Overlay Networks (SSONs), coupled with the rapid development of multimedia applications make the management of such technologies a major challenge. This thesis investigates the SSONs management problem and proposes an autonomic architecture, a self-organizing and self-adapting algorithm, and a utility function for monitoring the Quality of Experience (QoE) of IPTV streams in SSONs. First, we examine the different issues stemming from the autonomic management of SSONs and identify the limitations of existing approaches. We then propose an architecture to ease the management of SSONs by incorporating autonomic computing principles to make SSONs acquire self-management capabilities. The proposed architecture introduces autonomic control loops that continuously monitor network components and analyze the gathered data. An Autonomic System (AS) is comprised of one or more Autonomic Managers (AM) which take control of managing other elements in the network. The proposed architecture highlights the different components of an AM and identifies its purpose. The distributed nature of the proposed architecture avoids limitations of centralized management solutions. We then propose a scheme to allow AMs to emerge among the set of nodes in the network as the most powerful ones in terms of different factors, including processing capabilities and stability. Using a self-organizing and self-adapting distributed protocol, each node in the overlay selects an appropriate AM to report to so that sensed data is delivered error-free, and in a timely manner, while the load is distributed over the AMs. Finally, we propose a utility function to monitor the quality of IPTV streams by predicting QoE based on statistical Quality of Service (QoS) information. The proposed function is simple and does not require high processing power. It allows the QoE of IPTV users to be monitored in real-time by the AMs, so that quality degradations are accurately identified and adaptation mechanisms are triggered at the right moment to correct issues causing degradations. Theoretical analysis and simulations studies are presented to demonstrate the performance of the proposed schemes.

Recent years the research focus began on “Quality of Experience” (QoE) that addresses user satisfaction level and improvement of service. The notation sustainable throughput, sometimes also called reliable throughput, ensures user satisfaction level at the same time requires an optimum resource to provide the service. In the context of communication, it becomes important to analyze user behavior with respect to network performance.             Since the user is closer to the transport layer than the network layer, there opens a new domain to relate “QoE aware sustainable throughput” and “TCP throughput”. There is a need to further investigation of “QoE aware sustainable throughput” as it the one which sufficiently QoE, while “TCP throughput” is the result of a control process on a layer. Moreover, it is essential to estimate the QoE aware sustainable throughput based on HTTP streaming on the server and client application may result in a closer understanding of the nature of TCP in terms of user expectation.                In this study, we evaluated the performance of video streaming considering the TCP throughput in the presence of network disturbances, packet loss, and delay. The TCP packet behavior is observed in the experimental test setup. The quality assessment at which the QoE problems can still be kept at the desired level is determined. Mean opinion scores of the preferred use cases for the dash and non-dash server is used to estimate the relationship factor between “TCP throughput” and “QoE aware sustainable throughput”.

Made available in DSpace on 2014-06-12T15:48:27Z (GMT). No. of bitstreams: 2 arquivo1249_1.pdf: 1963665 bytes, checksum: 4d41d19894034bb41cfe58785e1d816a (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2011 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / Usuários de aplicações avançadas, tais os de grade computacional, encontram algumas vezes problemas de desempenho de rede que frustram as suas expectativas de realização de atividades. Por isso, a assistência para comunidades de usuários que tem dificuldades em usar a rede tem sido identificada como uma das principais questões de suporte relacionadas ao desempenho. Isso explica a crescente demanda por serviços de monitoramento que tentam auxiliar os usuários no entendimento das propriedades de rede, bem como no uso de suas aplicações. Serviços de monitoramento trouxeram grandes benefícios em termos de suporte aos usuários. Mesmo assim, existe uma carência por serviços de aconselhamento que beneficie os usuários com informações que vão além da simples apresentação das propriedades de rede. Atualmente, tanto usuários avançados quanto equipes de suporte contam com ferramentas que apresentam informações de uma perspectiva puramente técnica. A maioria delas se baseiam em informações obtidas como resultado de atividades de monitoramento, independentemente dos níveis de qualidade de serviços percebidos pelos usuários. Tais ferramentas de medição atendem razoavelmente os requisitos dos gerentes, operadores e usuários finais quando se trata de aplicações comuns. Contudo, a comunidade de pesquisa vem destacando a importância da qualidade de experiência (QoE) durante a avaliação dos serviços de rede quando se trata de aplicações avançadas. Ao considerar as opiniões dos usuários, o gerenciamento da qualidade de serviços torna-se mais efetivo, uma vez que a avaliação do serviço não está restrita às considerações da equipe de suporte e gerenciamento. Essa tese apresenta uma abordagem de aconselhamento para o suporte de usuários de rede que foca na colaboração entre usuários através do compartilhamento das suas experiências no uso das aplicações para construir uma base de conhecimento. O conhecimento representado inclui não somente a informação sobre os problemas de desempenho, como também as características as aplicações e as opiniões dos usuários. Através dessas características, a abordagem procura alcançar altos níveis de satisfação dos usuários pela redução gradual do volume de informações de suporte erradas. Para avaliar a viabilidade dessa abordagem, um modelo de sistema de raciocínio baseado em casos (RBC) foi construído e validado através de um estudo experimental conduzido em laboratório por meio de um sistema multiagente. O sistema é apoiado por uma base de conhecimento baseada no uso de ontologias e um esquema de reputação baseado no algoritmo Pagerank. Os resultados do estudo mostraram a efetividade da abordagem proposta, sua resilência à comportamento de usuários incoerentes e em conluio, e a importância do conhecimento do domínio sobre as atividades de suporte a usuários

In recent years, there has been a significant increase in the demand for streaming of high quality videos on the smart mobile phones. In order to meet the user quality requirements, it is important to maintain the end user quality while taking the resource consumption into consideration. This demand caught the attention of the research communities and network providers to prioritize Quality of Experience (QoE) in addition to the Quality of Service (QoS). In order to meet the users’ expectations, the QoE studies have gained utmost importance, thus creating the challenge of evaluating it in such a way that the quality, cost and energy consumption are taken into account. This gave way to the concept of QoE-aware sustainable throughput, which denotes the maximal throughput at which QoE problems can be still kept at a desired level. The aim of the thesis is to determine the sustainable throughput values from the QoE perspective. The values are observed for different delay and packet loss values in wireless and mobile scenarios. The evaluation is done using the subjective video quality assessment method. In the subjective assessment method, the evaluation is done using the ITU-T recommended Absolute Category Rating (ACR). The video quality ratings are taken from the users, and are then averaged to obtain the Mean Opinion Score (MOS). The obtained scores are used for analysis in determining the sustainable throughput values from the users’ perspective. From the results it is determined that, for all the video test cases, the videos are rated better quality at low packet loss values and low delay values. The quality of the videos with the presence of delay is rated high compared to the video quality in the case of packet loss. It was observed that the high resolution videos are feeble in the presence of higher disturbances i.e. high packet loss and larger delays. From considering all the cases, it can be observed that the QoE disturbances due to the delivery issues is at an acceptable minimum for the 360px video. Hence, the 480x360 video is the threshold to sustain the video quality.

Quality of Experience (QoE) deals with the acceptance of a service quality by the users and has evolved significantly as an important concept over the past 10 years. Network operators and service providers have gained interest in QoE-aware management of networks, in order to better fulfill end-user demands and gain a competitive edge in the market. While this growth promises new business opportunities, it also presents several challenges to the networking researchers, which are mainly related to the assessment of user experience. Several QoE assessment models have been proposed to estimate the user satisfaction for a given service quality. Most of them are intrusive and require knowledge of the content reference. In contrast, the network operators require non-intrusive methods, which allow models to be implementable on the network-level without having much knowledge about that reference. The methods should be able to monitor QoE passively in real-time, based on the information readily available on network level. This thesis investigates indicators, which are intended to be used in the development of non-intrusive network-based methods for the real-time QoE assessment and monitoring. First, a bridge is made between the user and the network perspectives by correlating the user traffic characteristics measured on an operational network and user subjective experience tested on an experimental platform. It is shown that the user session volume appears to be an indicator of users’ interest in the service. Second, the TCP connection interruptions are investigated as an indicator to infer the user experience. It is found out that the request-level performance metrics show stronger correlations between the interruption rates and the network Quality of Service (QoS). Third, a wavelet-based criterion is devised to assist in the identification of those traffic gaps, which may result in the degradation of QoE. It can be implemented on the network-level in quasi-real-time to quickly identify the user-perceived performance issues.

The Cloud infrastructure has become an ideal platform for large-scale applications, such as Video-on-Demand (VoD). As VoD systems migrate to the Cloud, new challenges emerge. The complexity of the Cloud system due to virtualization and resource sharing complicates the Quality of Experience (QoE) management. Operational failures in the Cloud can lead to session crashes. In addition to the Cloud, there are many other systems involved in the large-scale video streaming. These systems include the Content Delivery Networks (CDNs), multiple transit networks, access networks, and user devices. Anomalies in any of these systems can affect users’ Quality of Experience (QoE). Identifying the anomalous system that causes QoE degradation is challenging for VoD providers due to their limited visibility over these systems. We propose to apply end user QoE in the management and control of large-scale VoD systems in the Cloud. We present a QoE-based management and control systems and validate them in production Clouds. QMan, a QoE based Management system for VoD in the Cloud, controls the server selection adaptively based on user QoE. QWatch, a scalable monitoring system, detects and locates anomalies based on the end-user QoE. QRank, a scalable anomaly identification system, identifies the anomalous systems causing QoE anomalies. The proposed systems are developed and evaluated in production Clouds (Microsoft Azure, Google Cloud and Amazon Web Service). QMan provides 30% more users with QoE above the “good” Mean Opinion Score (MOS) than existing server selection systems. QMan discovers operational failures by QoE based server monitoring and prevents streaming session crashes. QWatch effectively detects and locates QoE anomalies in our extensive experiments in production Clouds. We find numerous false positives and false negatives when system metric based anomaly detection methods are used. QRank identifies anomalous systems causing 99.98% of all QoE anomalies among transit networks, access networks and user devices. Our extensive experiments in production Clouds show that transit networks are the most common bottleneck causing QoE anomalies. Cloud provider should identify bottleneck transit networks and determine appropriate peering with Internet Service Providers (ISPs) to bypass these bottlenecks.

The constant growth in network communications technologies and the emergence of Service Specific Overlay Networks (SSONs), coupled with the rapid development of multimedia applications make the management of such technologies a major challenge. This thesis investigates the SSONs management problem and proposes an autonomic architecture, a self-organizing and self-adapting algorithm, and a utility function for monitoring the Quality of Experience (QoE) of IPTV streams in SSONs. First, we examine the different issues stemming from the autonomic management of SSONs and identify the limitations of existing approaches. We then propose an architecture to ease the management of SSONs by incorporating autonomic computing principles to make SSONs acquire self-management capabilities. The proposed architecture introduces autonomic control loops that continuously monitor network components and analyze the gathered data. An Autonomic System (AS) is comprised of one or more Autonomic Managers (AM) which take control of managing other elements in the network. The proposed architecture highlights the different components of an AM and identifies its purpose. The distributed nature of the proposed architecture avoids limitations of centralized management solutions. We then propose a scheme to allow AMs to emerge among the set of nodes in the network as the most powerful ones in terms of different factors, including processing capabilities and stability. Using a self-organizing and self-adapting distributed protocol, each node in the overlay selects an appropriate AM to report to so that sensed data is delivered error-free, and in a timely manner, while the load is distributed over the AMs. Finally, we propose a utility function to monitor the quality of IPTV streams by predicting QoE based on statistical Quality of Service (QoS) information. The proposed function is simple and does not require high processing power. It allows the QoE of IPTV users to be monitored in real-time by the AMs, so that quality degradations are accurately identified and adaptation mechanisms are triggered at the right moment to correct issues causing degradations. Theoretical analysis and simulations studies are presented to demonstrate the performance of the proposed schemes.

With the development of mobile networks, customer needs and behaviourshave changed. Mobile communications means so much more than simplevoice communication; there is now mobile Internet with web surfing, videophone,streaming media, and micro blogging. The objective of network optimizationhas gradually shifted from enhancing network performance to improvequality of experience (QoE). Therefore, assessing and optimizing QoEis the trend for optimizing future mobile networks. Today, users want reliable access for their content, wherever they go inthe network. To deliver the best possible experience to mobile broadbandsubscribers, operators need new ways to assess performance that will enablethem to build and manage their networks in the most efficient way. The newparadigmatic eco system (user-interface-network-content) requires novel anddisruptive end-to-end considerations in order to enable and sustain the nextgeneration of services and user experience. Thus, the extraordinary adoptionof mobile connectivity by end users, and the need for optimized bandwidthmanagement network resource, on the one hand, and the growing interest forgood quality content delivery/consumption, is boosting the creation of newnetwork solutions. We consider that by taking advantage of the capacity to support multimedia platforms and applications of mobile devices (e.g. smartphones, tablets,etc.) is possible to incorporate and provide awareness to the wireless infrastructuresin the context of cross-layer systems to manage the resource allocationaccording to expected QoE levels. In this thesis, we address the questionon how to implement QoE-aware mobile networks and evaluate differentschedulers oriented to take advantage of the proposed architecture. We showthat by providing QoE-awareness to the network infrastructure is possible toimprove user’s QoE and generate impacts in the utilization of the networkresources. With this study, we provide insights into the broader question ofwhether future mobile infrastructures can be deployed considering QoE besidesthe classical QoS considerations. / <p>QC 20140521</p>

The thesis focuses on Quality of Experience (QoE) of HTTP adaptive video streaming (HAS) and traffic management in access networks to improve the QoE of HAS. First, the QoE impact of adaptation parameters and time on layer was investigated with subjective crowdsourcing studies. The results were used to compute a QoE-optimal adaptation strategy for given video and network conditions. This allows video service providers to develop and benchmark improved adaptation logics for HAS. Furthermore, the thesis investigated concepts to monitor video QoE on application and network layer, which can be used by network providers in the QoE-aware traffic management cycle. Moreover, an analytic and simulative performance evaluation of QoE-aware traffic management on a bottleneck link was conducted. Finally, the thesis investigated socially-aware traffic management for HAS via Wi-Fi offloading of mobile HAS flows. A model for the distribution of public Wi-Fi hotspots and a platform for socially-aware traffic management on private home routers was presented. A simulative performance evaluation investigated the impact of Wi-Fi offloading on the QoE and energy consumption of mobile HAS. / Die Doktorarbeit beschäftigt sich mit Quality of Experience (QoE) – der subjektiv empfundenen Dienstgüte – von adaptivem HTTP Videostreaming (HAS) und mit Verkehrsmanagement, das in Zugangsnetzwerken eingesetzt werden kann, um die QoE des adaptiven Videostreamings zu verbessern. Zuerst wurde der Einfluss von Adaptionsparameters und der Zeit pro Qualitätsstufe auf die QoE von adaptivem Videostreaming mittels subjektiver Crowdsourcingstudien untersucht. Die Ergebnisse wurden benutzt, um die QoE-optimale Adaptionsstrategie für gegebene Videos und Netzwerkbedingungen zu berechnen. Dies ermöglicht Dienstanbietern von Videostreaming verbesserte Adaptionsstrategien für adaptives Videostreaming zu entwerfen und zu benchmarken. Weiterhin untersuchte die Arbeit Konzepte zum Überwachen von QoE von Videostreaming in der Applikation und im Netzwerk, die von Netzwerkbetreibern im Kreislauf des QoE-bewussten Verkehrsmanagements eingesetzt werden können. Außerdem wurde eine analytische und simulative Leistungsbewertung von QoE-bewusstem Verkehrsmanagement auf einer Engpassverbindung durchgeführt. Schließlich untersuchte diese Arbeit sozialbewusstes Verkehrsmanagement für adaptives Videostreaming mittels WLAN Offloading, also dem Auslagern von mobilen Videoflüssen über WLAN Netzwerke. Es wurde ein Modell für die Verteilung von öffentlichen WLAN Zugangspunkte und eine Plattform für sozialbewusstes Verkehrsmanagement auf privaten, häuslichen WLAN Routern vorgestellt. Abschließend untersuchte eine simulative Leistungsbewertung den Einfluss von WLAN Offloading auf die QoE und den Energieverbrauch von mobilem adaptivem Videostreaming.